technical breakdown last crawled: this week · source: prompt & output audits
LLM retrieval & citation patterns
Index Progress & Audit Level
74%
Mapping how vector embeddings and semantic proximity influence whether AI tools cite enterprise documentation.
This technical breakdown documents key findings from running prompt audits and structured entity schema tests across enterprise search workloads.
Core Technical Hypotheses
Vector distance threshold decreases by 18% with structured JSON-LD.
LLMs favor direct Q&A headers over dense marketing narrative.
Implementation Snippet
schema-example.jsonld
{
"@context": "https://schema.org",
"@type": "TechArticle",
"name": "LLM retrieval & citation patterns",
"author": {
"@type": "Person",
"name": "Lalitesh Pawar"
}
}